Objectives: The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. Methods: Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub-datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation. Results: We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96. Conclusions: ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.
Objectives: The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. Methods: Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub-datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation. Results: We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96. Conclusions: ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.
Authors: Alexandra A de Souza; Danilo Candido de Almeida; Thiago S Barcelos; Rodrigo Campos Bortoletto; Roberto Munoz; Helio Waldman; Miguel Angelo Goes; Leandro A Silva Journal: Soft comput Date: 2021-05-17 Impact factor: 3.732
Authors: Espen Jimenez-Solem; Tonny S Petersen; Casper Hansen; Christian Hansen; Christina Lioma; Christian Igel; Wouter Boomsma; Oswin Krause; Stephan Lorenzen; Raghavendra Selvan; Janne Petersen; Martin Erik Nyeland; Mikkel Zöllner Ankarfeldt; Gert Mehl Virenfeldt; Matilde Winther-Jensen; Allan Linneberg; Mostafa Mehdipour Ghazi; Nicki Detlefsen; Andreas David Lauritzen; Abraham George Smith; Marleen de Bruijne; Bulat Ibragimov; Jens Petersen; Martin Lillholm; Jon Middleton; Stine Hasling Mogensen; Hans-Christian Thorsen-Meyer; Anders Perner; Marie Helleberg; Benjamin Skov Kaas-Hansen; Mikkel Bonde; Alexander Bonde; Akshay Pai; Mads Nielsen; Martin Sillesen Journal: Sci Rep Date: 2021-02-05 Impact factor: 4.379
Authors: Patrick A Gladding; Zina Ayar; Kevin Smith; Prashant Patel; Julia Pearce; Shalini Puwakdandawa; Dianne Tarrant; Jon Atkinson; Elizabeth McChlery; Merit Hanna; Nick Gow; Hasan Bhally; Kerry Read; Prageeth Jayathissa; Jonathan Wallace; Sam Norton; Nick Kasabov; Cristian S Calude; Deborah Steel; Colin Mckenzie Journal: Future Sci OA Date: 2021-06-12